Abstract

OBJECTIVES:

We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data.

METHODS:

The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data.

RESULTS:

Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested.

CONCLUSION:

The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.

Power vs. critical values of the Wald testing P-values as a test statistic for a simulated data set with a quantitative trait influenced by a gene and an environmental factor. The simulating values of the genetic effects were . The corresponding statistical genetic effects were [102.33, 5.37, 1.33, 3.98, 3.05, 1.5]. The allele frequency and exposure frequency were 0.15 and 0.22, respectively. The simulating residual variance is 144.0.

Power vs. critical values of the Wald testing P-values as a test statistic for a simulated data set with a quantitative trait influenced by a gene and an environmental factor. The simulating values of the genetic effects were . The corresponding statistical genetic effects were [101.71, 5.40, 2.00, 0.00, 0.00, 0.00]. The allele frequency and exposure frequency were 0.15 and 0.22, respectively. The simulating residual variance is 144.0.

Power vs. critical values of the Wald testing P-values as a test statistic for a simulated case-control data set with GxE interaction for all replicates. The simulating values of the genetic effects were . The actual sample values of the genetic effects were [−0.28, 0.30, 0.10, 0.20, 0.11, 0.03]. The corresponding statistical genetic effects were [−0.01, 0.38, 0.10, 0.27, 0.13, 0.03]. The allele frequency and exposure frequency were 0.25 and 0.25 respectively.

Power vs. critical values of the Wald testing P-values as a test statistic for a simulated case-control data set with GxE interaction for all 1000 replicates. The simulating values of the genetic effects were . The actual sample values were [−0.31, 0.40, 0.19, 0.00, 0.01, −0.01]. The corresponding statistical genetic effect were [0.00, 0.49, 0.19, 0.00, 0.00, −0.01]. The allele frequency and exposure frequency were 0.25 and 0.25, respectively.